{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/mi_9.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}24 Aug 2020, 02:36:53
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_raw.dta", clear
. {c )-}
{txt}
{com}. 
. if ("`model'" == "mi") {c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_imputed_M10.dta", clear  
. {c )-}
{txt}
{com}. 
. * for now, we use the following simple survey weights 
. if ("`model'" == "mi") {c -(}
.         mi svyset `geo_type' [pw=ObsWgt0] 
{res}
      {txt}pweight:{col 16}{res}ObsWgt0
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}county
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}
{res}{com}. {c )-}
{txt}
{com}. else {c -(}
.         svyset `geo_type' [pw=ObsWgt0]  
. {c )-}
{txt}
{com}. 
. * margins for each category
. program margin_interact 
{txt}  1{com}.         args X Y k model
{txt}  2{com}.         sum `X', d 
{txt}  3{com}.         local gap = (`r(max)' - `r(min)') / `k' 
{txt}  4{com}.         if ("`model'" == "logit") {c -(}
{txt}  5{com}.                 margin `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  6{com}.         {c )-} 
{txt}  7{com}.         else if ("`model'" == "mi") {c -(}
{txt}  8{com}.                 mimrgns `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  9{com}.         {c )-}       
{txt} 10{com}. end 
{txt}
{com}. 
. program mchange_mi
{txt}  1{com}.         args X k model
{txt}  2{com}.         if ("`model'" == "logit") {c -(}
{txt}  3{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt}  4{com}.                         sum `X' if e(sample), d 
{txt}  5{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt}  6{com}.                         mlincom  2 - 1, decimal(7) stat(all)    
{txt}  7{com}.                 {c )-} 
{txt}  8{com}.                 else if ("`k'" == "binary") {c -(}
{txt}  9{com}.                         sum `X' if e(sample), d 
{txt} 10{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 11{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 12{com}.                 {c )-} 
{txt} 13{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 14{com}.                         margin `X' if e(sample)==1 , at() pwcompare predict(pr) 
{txt} 15{com}.                 {c )-}
{txt} 16{com}.         {c )-}
{txt} 17{com}. 
.         else if ("`model'" == "mi") {c -(}
{txt} 18{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt} 19{com}.                         sum `X' , d 
{txt} 20{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 21{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 22{com}.                 {c )-} 
{txt} 23{com}.                 else if ("`k'" == "binary") {c -(}
{txt} 24{com}.                         sum `X' , d 
{txt} 25{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 26{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 27{com}.                 {c )-} 
{txt} 28{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 29{com}.                         mimrgns `X' , at()   pwcompare predict(pr)      
{txt} 30{com}.                 {c )-}
{txt} 31{com}.         {c )-}
{txt} 32{com}. end
{txt}
{com}. 
. * create some dummy codings
. tab Race5, gen(race5_nh)

      {txt}Race5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}107,831,845       79.53       79.53
{txt}          2 {c |}{res} 14,631,144       10.79       90.32
{txt}          3 {c |}{res}  2,867,304        2.11       92.44
{txt}          4 {c |}{res}  2,753,311        2.03       94.47
{txt}          5 {c |}{res}  7,499,129        5.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}135,582,733      100.00
{txt}
{com}.         rename race5_nh1 White_nh 
{res}{txt}
{com}.         rename race5_nh2 Black_nh 
{res}{txt}
{com}.         rename race5_nh3 AIAN_nh 
{res}{txt}
{com}.         rename race5_nh4 AsPI_nh 
{res}{txt}
{com}.         rename race5_nh5 Hispanic
{res}{txt}
{com}. 
. tab MarStat5, gen(ms)

   {txt}MarStat5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res} 75,689,936       55.83       55.83
{txt}          2 {c |}{res} 10,166,664        7.50       63.32
{txt}          3 {c |}{res} 13,925,509       10.27       73.60
{txt}          4 {c |}{res}  2,609,308        1.92       75.52
{txt}          5 {c |}{res} 33,190,855       24.48      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}135,582,272      100.00
{txt}
{com}.         rename ms1 Marrd5
{res}{txt}
{com}.         rename ms2 Widow5
{res}{txt}
{com}.         rename ms3 Divor5
{res}{txt}
{com}.         rename ms4 Separ5
{res}{txt}
{com}.         rename ms5 NvMar5
{res}{txt}
{com}. 
. tab AgeGrp4, gen(ag) 

    {txt}AgeGrp4 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res} 21,231,562       15.66       15.66
{txt}          2 {c |}{res} 39,043,312       28.80       44.46
{txt}          3 {c |}{res} 47,351,260       34.92       79.38
{txt}          4 {c |}{res} 27,956,599       20.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}135,582,733      100.00
{txt}
{com}.         rename ag1 Age_15_24
{res}{txt}
{com}.         rename ag2 Age_25_44
{res}{txt}
{com}.         rename ag3 Age_45_64
{res}{txt}
{com}.         rename ag4 Age_65_Up
{res}{txt}
{com}. 
. destring St, replace 
{txt}St: all characters numeric; {res}replaced {txt}as {res}byte
{txt}
{com}. 
. * set-up equations
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Cath Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local demographics_raw i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
{txt}
{com}. local demographics_individual i.Female i.AgeGrp4 i.Race5 i.BornUSA i.MarStat5 
{txt}
{com}. local demographics_county c.RAT_Female c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4  c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 c.RAT_BornUSA c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
{txt}
{com}. local demographics_same i.Female c.std_same_prop_Sex i.AgeGrp4 c.std_same_prop_AgeGrp4 i.Race5 c.std_same_prop_Race5 i.BornUSA c.std_same_prop_BornUSA i.MarStat5 c.std_same_prop_MarStat5 
{txt}
{com}. local demographics_inter i.Female##c.std_same_prop_Sex i.AgeGrp4##c.std_same_prop_AgeGrp4 i.Race5##c.std_same_prop_Race5 i.BornUSA##c.std_same_prop_BornUSA i.MarStat5##c.std_same_prop_MarStat5
{txt}
{com}. 
. 
. if (`model_version' == 1){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' 
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 
. {c )-}
{txt}
{com}. if (`model_version' == 2){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' i.UnEmpl c.RAT_UnEmpl i.PhysProb c.RAT_PhysProb
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 3){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' 
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 
. {c )-}
{txt}
{com}. if (`model_version' == 4){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' i.UnEmpl c.std_same_prop_UnEmpl i.PhysProb c.std_same_prop_PhysProb
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 UnEmpl std_same_prop_UnEmpl PhysProb std_same_prop_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 5){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' 
. {c )-}
{txt}
{com}. if (`model_version' == 6){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' i.UnEmpl##c.std_same_prop_UnEmpl i.PhysProb##c.std_same_prop_PhysProb
. {c )-}       
{txt}
{com}. 
. if (`model_version' == 7){c -(}
.         local model_eq i.Year `demographics_individual' `contextual_control' `religion' 
.         local margin_demographics Female AgeGrp4 Race5 BornUSA MarStat5 
. {c )-}
{txt}
{com}. if (`model_version' == 8){c -(}
.         local model_eq i.Year `demographics_county' `contextual_control' `religion' 
.         local margin_demographics RAT_Female RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 RAT_BornUSA RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 
. {c )-}
{txt}
{com}. 
. if (`model_version' == 9){c -(}
.         local model_eq i.Year `demographics_individual' `contextual_control' `religion'  i.UnEmpl i.PhysProb
.         local margin_demographics Female AgeGrp4 Race5 BornUSA MarStat5 UnEmpl PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 10){c -(}
.         local model_eq i.Year `demographics_county' `contextual_control' `religion'  c.RAT_UnEmpl c.RAT_PhysProb
.         local margin_demographics RAT_Female RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 RAT_BornUSA RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 RAT_UnEmpl RAT_PhysProb
. {c )-}
{txt}
{com}. 
. * test how long it would take.
. * mi estimate: svy: mean Suic 
. * local demographics i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
. * mi estimate: svy: logit Suic i.St `demographics' UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. 
. * main effects : margins
. if ("`model'" == "mi"){c -(}
. 
.         mi estimate: svy: logit Suic i.St `model_eq', or 
{res}
{txt}Multiple-imputation estimates{col 47}Imputations{col 65}= {res}          10
{txt}Survey: Logistic regression{col 47}Number of obs{col 65}= {res}  11,814,307

{txt}{col 1}Number of strata{col 19}= {res}        1{txt}{col 47}Population size{col 65}={res}  418,786,443
{txt}{col 1}Number of PSUs{col 19}= {res}      918
{txt}{col 47}Average RVI{col 65}= {res}      0.0151
{txt}{col 47}Largest FMI{col 65}= {res}      0.3298
{txt}{col 47}Complete DF{col 65}= {res}         917
{txt}DF adjustment:{ralign 15: {res:Small sample}}{col 47}DF:     min{col 65}= {res}       79.41
{txt}{col 47}        avg{col 65}= {res}      880.05
{txt}{col 47}        max{col 65}= {res}      914.94
{txt}Model F test:{ralign 16: {res:Equal FMI}}{col 47}F({res}  44{txt},{res}  914.5{txt}){col 65}= {res}      882.72
{txt}Within VCE type: {ralign 12:{res:Linearized}}{col 47}Prob > F{col 65}= {res}      0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Suic{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}St {c |}
{space 10}8  {c |}{col 14}{res}{space 2}-.0470108{col 26}{space 2} .0924875{col 37}{space 1}   -0.51{col 46}{space 3}0.611{col 54}{space 4} -.228523{col 67}{space 3} .1345014
{txt}{space 9}13  {c |}{col 14}{res}{space 2} -.437461{col 26}{space 2} .0928538{col 37}{space 1}   -4.71{col 46}{space 3}0.000{col 54}{space 4}-.6196922{col 67}{space 3}-.2552297
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.5380687{col 26}{space 2} .0953256{col 37}{space 1}   -5.64{col 46}{space 3}0.000{col 54}{space 4}-.7251509{col 67}{space 3}-.3509865
{txt}{space 9}24  {c |}{col 14}{res}{space 2} -.551678{col 26}{space 2} .0951471{col 37}{space 1}   -5.80{col 46}{space 3}0.000{col 54}{space 4}-.7384101{col 67}{space 3} -.364946
{txt}{space 9}25  {c |}{col 14}{res}{space 2} -.866345{col 26}{space 2} .0924851{col 37}{space 1}   -9.37{col 46}{space 3}0.000{col 54}{space 4}-1.047853{col 67}{space 3}-.6848373
{txt}{space 9}34  {c |}{col 14}{res}{space 2}-.7763504{col 26}{space 2} .0943275{col 37}{space 1}   -8.23{col 46}{space 3}0.000{col 54}{space 4}-.9614738{col 67}{space 3}-.5912269
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .1259755{col 26}{space 2} .0959488{col 37}{space 1}    1.31{col 46}{space 3}0.190{col 54}{space 4}-.0623299{col 67}{space 3} .3142809
{txt}{space 9}37  {c |}{col 14}{res}{space 2}-.3797051{col 26}{space 2} .0965033{col 37}{space 1}   -3.93{col 46}{space 3}0.000{col 54}{space 4}-.5690987{col 67}{space 3}-.1903116
{txt}{space 9}40  {c |}{col 14}{res}{space 2}-.5006132{col 26}{space 2} .1022586{col 37}{space 1}   -4.90{col 46}{space 3}0.000{col 54}{space 4}-.7013018{col 67}{space 3}-.2999245
{txt}{space 9}41  {c |}{col 14}{res}{space 2}-.2927699{col 26}{space 2} .0932645{col 37}{space 1}   -3.14{col 46}{space 3}0.002{col 54}{space 4}-.4758071{col 67}{space 3}-.1097327
{txt}{space 9}44  {c |}{col 14}{res}{space 2}-.7144258{col 26}{space 2} .0938761{col 37}{space 1}   -7.61{col 46}{space 3}0.000{col 54}{space 4}-.8986633{col 67}{space 3}-.5301883
{txt}{space 9}45  {c |}{col 14}{res}{space 2} -.406314{col 26}{space 2} .0953576{col 37}{space 1}   -4.26{col 46}{space 3}0.000{col 54}{space 4} -.593459{col 67}{space 3} -.219169
{txt}{space 9}49  {c |}{col 14}{res}{space 2}  .609621{col 26}{space 2}   .17182{col 37}{space 1}    3.55{col 46}{space 3}0.000{col 54}{space 4} .2724139{col 67}{space 3} .9468281
{txt}{space 9}51  {c |}{col 14}{res}{space 2}-.2237125{col 26}{space 2} .0919427{col 37}{space 1}   -2.43{col 46}{space 3}0.015{col 54}{space 4}-.4041557{col 67}{space 3}-.0432693
{txt}{space 9}55  {c |}{col 14}{res}{space 2}-.5166155{col 26}{space 2} .0945052{col 37}{space 1}   -5.47{col 46}{space 3}0.000{col 54}{space 4}-.7020876{col 67}{space 3}-.3311433
{txt}{space 12} {c |}
{space 8}Year {c |}
{space 7}2006  {c |}{col 14}{res}{space 2}-.0367614{col 26}{space 2} .0182594{col 37}{space 1}   -2.01{col 46}{space 3}0.044{col 54}{space 4}-.0725968{col 67}{space 3}-.0009261
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} .0376218{col 26}{space 2} .0188795{col 37}{space 1}    1.99{col 46}{space 3}0.047{col 54}{space 4} .0005695{col 67}{space 3} .0746741
{txt}{space 7}2008  {c |}{col 14}{res}{space 2}  .045688{col 26}{space 2}   .02052{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0054162{col 67}{space 3} .0859597
{txt}{space 7}2009  {c |}{col 14}{res}{space 2}-.0174175{col 26}{space 2} .0197134{col 37}{space 1}   -0.88{col 46}{space 3}0.377{col 54}{space 4} -.056107{col 67}{space 3}  .021272
{txt}{space 7}2010  {c |}{col 14}{res}{space 2}-.0578796{col 26}{space 2} .0216443{col 37}{space 1}   -2.67{col 46}{space 3}0.008{col 54}{space 4}-.1003592{col 67}{space 3}   -.0154
{txt}{space 7}2011  {c |}{col 14}{res}{space 2}-.0007898{col 26}{space 2} .0228892{col 37}{space 1}   -0.03{col 46}{space 3}0.972{col 54}{space 4} -.045712{col 67}{space 3} .0441324
{txt}{space 12} {c |}
{space 4}1.Female {c |}{col 14}{res}{space 2}-1.342316{col 26}{space 2} .0154011{col 37}{space 1}  -87.16{col 46}{space 3}0.000{col 54}{space 4}-1.372542{col 67}{space 3} -1.31209
{txt}{space 12} {c |}
{space 5}AgeGrp4 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .8143664{col 26}{space 2} .0230313{col 37}{space 1}   35.36{col 46}{space 3}0.000{col 54}{space 4} .7691651{col 67}{space 3} .8595676
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .9449531{col 26}{space 2}  .027789{col 37}{space 1}   34.00{col 46}{space 3}0.000{col 54}{space 4} .8904128{col 67}{space 3} .9994934
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .8058517{col 26}{space 2} .0355687{col 37}{space 1}   22.66{col 46}{space 3}0.000{col 54}{space 4} .7360217{col 67}{space 3} .8756816
{txt}{space 12} {c |}
{space 7}Race5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-1.145184{col 26}{space 2}  .025077{col 37}{space 1}  -45.67{col 46}{space 3}0.000{col 54}{space 4}  -1.1944{col 67}{space 3}-1.095967
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1016014{col 26}{space 2} .0779891{col 37}{space 1}   -1.30{col 46}{space 3}0.193{col 54}{space 4}  -.25466{col 67}{space 3} .0514572
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.4118298{col 26}{space 2} .0444762{col 37}{space 1}   -9.26{col 46}{space 3}0.000{col 54}{space 4} -.499117{col 67}{space 3}-.3245425
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.7634311{col 26}{space 2} .0378064{col 37}{space 1}  -20.19{col 46}{space 3}0.000{col 54}{space 4}-.8376286{col 67}{space 3}-.6892336
{txt}{space 12} {c |}
{space 3}1.BornUSA {c |}{col 14}{res}{space 2} .4430487{col 26}{space 2} .0341647{col 37}{space 1}   12.97{col 46}{space 3}0.000{col 54}{space 4} .3759983{col 67}{space 3}  .510099
{txt}{space 12} {c |}
{space 4}MarStat5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .6935612{col 26}{space 2} .0216316{col 37}{space 1}   32.06{col 46}{space 3}0.000{col 54}{space 4} .6511063{col 67}{space 3}  .736016
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .9606128{col 26}{space 2} .0145155{col 37}{space 1}   66.18{col 46}{space 3}0.000{col 54}{space 4} .9321131{col 67}{space 3} .9891125
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.3131085{col 26}{space 2} .0916518{col 37}{space 1}   -3.42{col 46}{space 3}0.001{col 54}{space 4}-.4929824{col 67}{space 3}-.1332347
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .6193753{col 26}{space 2} .0185953{col 37}{space 1}   33.31{col 46}{space 3}0.000{col 54}{space 4} .5828748{col 67}{space 3} .6558757
{txt}{space 12} {c |}
{space 1}Rat_Poverty {c |}{col 14}{res}{space 2} .0025591{col 26}{space 2} .0018173{col 37}{space 1}    1.41{col 46}{space 3}0.159{col 54}{space 4}-.0010074{col 67}{space 3} .0061257
{txt}{space 1}Rat_Mig_Cum {c |}{col 14}{res}{space 2}-1.202372{col 26}{space 2} .4939503{col 37}{space 1}   -2.43{col 46}{space 3}0.015{col 54}{space 4} -2.17178{col 67}{space 3}-.2329647
{txt}{space 5}Pop_Den {c |}{col 14}{res}{space 2}-.0000141{col 26}{space 2} 5.35e-06{col 37}{space 1}   -2.64{col 46}{space 3}0.008{col 54}{space 4}-.0000246{col 67}{space 3}-3.62e-06
{txt}{space 1}Rat_GC_ProE {c |}{col 14}{res}{space 2} .0000115{col 26}{space 2} 8.88e-06{col 37}{space 1}    1.29{col 46}{space 3}0.197{col 54}{space 4}-5.96e-06{col 67}{space 3} .0000289
{txt}{space 1}Rat_GC_ProM {c |}{col 14}{res}{space 2} 6.98e-06{col 26}{space 2} .0000112{col 37}{space 1}    0.62{col 46}{space 3}0.534{col 54}{space 4} -.000015{col 67}{space 3}  .000029
{txt}{space 1}Rat_GC_ProB {c |}{col 14}{res}{space 2}-3.53e-06{col 26}{space 2} .0000398{col 37}{space 1}   -0.09{col 46}{space 3}0.929{col 54}{space 4}-.0000816{col 67}{space 3} .0000745
{txt}{space 2}Rat_GC_Jew {c |}{col 14}{res}{space 2} .0000747{col 26}{space 2} .0000641{col 37}{space 1}    1.16{col 46}{space 3}0.244{col 54}{space 4}-.0000511{col 67}{space 3} .0002004
{txt}{space 2}Rat_GC_Oth {c |}{col 14}{res}{space 2}-.0000976{col 26}{space 2} .0000222{col 37}{space 1}   -4.39{col 46}{space 3}0.000{col 54}{space 4}-.0001412{col 67}{space 3}-.0000539
{txt}{space 4}1.UnEmpl {c |}{col 14}{res}{space 2}  1.93899{col 26}{space 2} .0438451{col 37}{space 1}   44.22{col 46}{space 3}0.000{col 54}{space 4} 1.851726{col 67}{space 3} 2.026255
{txt}{space 2}1.PhysProb {c |}{col 14}{res}{space 2}  .468067{col 26}{space 2} .0338528{col 37}{space 1}   13.83{col 46}{space 3}0.000{col 54}{space 4} .4016236{col 67}{space 3} .5345104
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-9.533823{col 26}{space 2}  .104095{col 37}{space 1}  -91.59{col 46}{space 3}0.000{col 54}{space 4}-9.738118{col 67}{space 3}-9.329527
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{com}.         estimates store m1 
. 
.         if (`model_version' <= 4 | `model_version' >= 9){c -(}
.                 if (`model_version' != 10){c -(}
.                         estimates restore m1
{txt}(results {stata estimates replay m1:m1} are active now)
{com}.                         mchange_mi Female "binary" "mi"

                           {txt}Female
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}135,582,733
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}  135582733

{txt}50%    {res}        1                      {txt}Mean          {res} .5158124
                        {txt}Largest       Std. Dev.     {res} .4997499
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res}   .24975
{txt}95%    {res}        1              1       {txt}Skewness      {res}-.0632814
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 1.004005

{txt}Multiple-imputation estimates{col 47}Imputations{col 65}= {res}          10
{txt}Predictive margins{col 47}Number of obs{col 65}= {res}  11,764,378

{txt}{col 1}Number of strata{col 19}= {res}        1{txt}{col 47}Population size{col 65}={res}  418,786,443
{txt}{col 1}Number of PSUs{col 19}= {res}      918{txt}{col 47}Subpop. no. obs{col 65}={res}   11,079,265
{txt}{col 47}Subpop. size{col 65}={res}            .
{txt}{col 47}Average RVI{col 65}= {res}      0.0031
{txt}{col 47}Largest FMI{col 65}= {res}      0.0044
{txt}{col 47}Complete DF{col 65}= {res}         917
{txt}DF adjustment:{ralign 15: {res:Small sample}}{col 47}DF:     min{col 65}= {res}      909.17
{txt}{col 47}        avg{col 65}= {res}      911.03
{txt}Within VCE type: {ralign 12:{res:Delta-method}}{col 47}        max{col 65}= {res}      912.90

{txt}Expression{col 14}: {res}Pr(Suic), predict(pr)

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:Female}{space 10}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:Female}{space 10}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0002394{col 26}{space 2} 2.07e-06{col 37}{space 1}  115.91{col 46}{space 3}0.000{col 54}{space 4} .0002353{col 67}{space 3} .0002434
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0000626{col 26}{space 2} 7.74e-07{col 37}{space 1}   80.80{col 46}{space 3}0.000{col 54}{space 4}  .000061{col 67}{space 3} .0000641
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 8:lincom}{space 1}{space 1}{ralign 8:se}{space 1}{space 1}{ralign 8:zvalue}{space 1}{space 1}{ralign 8:pvalue}{space 1}{space 1}{ralign 8:ll}{space 1}{space 1}{ralign 8:ul}{space 1}
{space 0}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}
{space 0}{space 0}{ralign 12:1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.0001768}}}{space 1}{space 1}{ralign 8:{res:{sf:0.0000022}}}{space 1}{space 1}{ralign 8:{res:{sf:-7.9e+01}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.0e+00}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.0001812}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.0001724}}}{space 1}
{com}.                         estimates restore m1
{txt}(results {stata estimates replay m1:m1} are active now)
{com}.                         mchange_mi AgeGrp4 "categorical" "mi"
{res}
{txt}Multiple-imputation estimates{col 47}Imputations{col 65}= {res}          10
{txt}Pairwise comparisons of predictive margins{col 47}Number of obs{col 65}= {res}  11,079,265

{txt}{col 1}Number of strata{col 19}= {res}        1{txt}{col 47}Population size{col 65}={res}  418,786,443
{txt}{col 1}Number of PSUs{col 19}= {res}      918{txt}{col 47}Subpop. no. obs{col 65}={res}   11,079,265
{txt}{col 47}Subpop. size{col 65}={res}            .
{txt}{col 47}Average RVI{col 65}= {res}      0.0178
{txt}{col 47}Largest FMI{col 65}= {res}      0.0656
{txt}{col 47}Complete DF{col 65}= {res}         917
{txt}DF adjustment:{ralign 15: {res:Small sample}}{col 47}DF:     min{col 65}= {res}      612.72
{txt}{col 47}        avg{col 65}= {res}      829.55
{txt}Within VCE type: {ralign 12:{res:Delta-method}}{col 47}        max{col 65}= {res}      910.22

{txt}Expression{col 14}: {res}Pr(Suic), predict(pr)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 5}AgeGrp4 {c |}
{space 5}2 vs 1  {c |}{col 14}{res}{space 2} .0000918{col 26}{space 2} 2.19e-06{col 37}{space 5} .0000875{col 51}{space 3} .0000961
{txt}{space 5}3 vs 1  {c |}{col 14}{res}{space 2} .0001147{col 26}{space 2} 2.95e-06{col 37}{space 5}  .000109{col 51}{space 3} .0001205
{txt}{space 5}4 vs 1  {c |}{col 14}{res}{space 2} .0000904{col 26}{space 2} 4.15e-06{col 37}{space 5} .0000822{col 51}{space 3} .0000985
{txt}{space 5}3 vs 2  {c |}{col 14}{res}{space 2}  .000023{col 26}{space 2} 2.51e-06{col 37}{space 5} .0000181{col 51}{space 3} .0000279
{txt}{space 5}4 vs 2  {c |}{col 14}{res}{space 2}-1.39e-06{col 26}{space 2} 3.95e-06{col 37}{space 5}-9.14e-06{col 51}{space 3} 6.36e-06
{txt}{space 5}4 vs 3  {c |}{col 14}{res}{space 2}-.0000244{col 26}{space 2} 3.31e-06{col 37}{space 5}-.0000309{col 51}{space 3}-.0000179
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{com}.                         estimates restore m1
{txt}(results {stata estimates replay m1:m1} are active now)
{com}.                         mchange_mi Race5 "categorical" "mi"
{res}
{txt}Multiple-imputation estimates{col 47}Imputations{col 65}= {res}          10
{txt}Pairwise comparisons of predictive margins{col 47}Number of obs{col 65}= {res}  11,079,265

{txt}{col 1}Number of strata{col 19}= {res}        1{txt}{col 47}Population size{col 65}={res}  418,786,443
{txt}{col 1}Number of PSUs{col 19}= {res}      918{txt}{col 47}Subpop. no. obs{col 65}={res}   11,079,265
{txt}{col 47}Subpop. size{col 65}={res}            .
{txt}{col 47}Average RVI{col 65}= {res}      0.0026
{txt}{col 47}Largest FMI{col 65}= {res}      0.0071
{txt}{col 47}Complete DF{col 65}= {res}         917
{txt}DF adjustment:{ralign 15: {res:Small sample}}{col 47}DF:     min{col 65}= {res}      904.03
{txt}{col 47}        avg{col 65}= {res}      910.95
{txt}Within VCE type: {ralign 12:{res:Delta-method}}{col 47}        max{col 65}= {res}      914.73

{txt}Expression{col 14}: {res}Pr(Suic), predict(pr)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 7}Race5 {c |}
{space 5}2 vs 1  {c |}{col 14}{res}{space 2}-.0001215{col 26}{space 2} 2.01e-06{col 37}{space 5}-.0001254{col 51}{space 3}-.0001175
{txt}{space 5}3 vs 1  {c |}{col 14}{res}{space 2}-.0000172{col 26}{space 2} .0000126{col 37}{space 5}-.0000418{col 51}{space 3} 7.44e-06
{txt}{space 5}4 vs 1  {c |}{col 14}{res}{space 2}-.0000601{col 26}{space 2} 5.38e-06{col 37}{space 5}-.0000707{col 51}{space 3}-.0000496
{txt}{space 5}5 vs 1  {c |}{col 14}{res}{space 2}-.0000951{col 26}{space 2} 3.39e-06{col 37}{space 5}-.0001017{col 51}{space 3}-.0000884
{txt}{space 5}3 vs 2  {c |}{col 14}{res}{space 2} .0001042{col 26}{space 2} .0000126{col 37}{space 5} .0000796{col 51}{space 3} .0001289
{txt}{space 5}4 vs 2  {c |}{col 14}{res}{space 2} .0000613{col 26}{space 2} 5.15e-06{col 37}{space 5} .0000512{col 51}{space 3} .0000714
{txt}{space 5}5 vs 2  {c |}{col 14}{res}{space 2} .0000264{col 26}{space 2} 3.25e-06{col 37}{space 5}   .00002{col 51}{space 3} .0000327
{txt}{space 5}4 vs 3  {c |}{col 14}{res}{space 2}-.0000429{col 26}{space 2} .0000136{col 37}{space 5}-.0000697{col 51}{space 3}-.0000161
{txt}{space 5}5 vs 3  {c |}{col 14}{res}{space 2}-.0000779{col 26}{space 2}  .000013{col 37}{space 5}-.0001035{col 51}{space 3}-.0000523
{txt}{space 5}5 vs 4  {c |}{col 14}{res}{space 2} -.000035{col 26}{space 2} 5.65e-06{col 37}{space 5}-.0000461{col 51}{space 3}-.0000239
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{com}.                         estimates restore m1
{txt}(results {stata estimates replay m1:m1} are active now)
{com}.                         mchange_mi BornUSA "binary" "mi"

                           {txt}BornUSA
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        1              0       {txt}Obs         {res}135,582,733
{txt}25%    {res}        1              0       {txt}Sum of Wgt. {res}  135582733

{txt}50%    {res}        1                      {txt}Mean          {res} .9265123
                        {txt}Largest       Std. Dev.     {res} .2609353
{txt}75%    {res}        1              1
{txt}90%    {res}        1              1       {txt}Variance      {res} .0680872
{txt}95%    {res}        1              1       {txt}Skewness      {res}-3.269105
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 11.68704

{txt}Multiple-imputation estimates{col 47}Imputations{col 65}= {res}          10
{txt}Predictive margins{col 47}Number of obs{col 65}= {res}  11,764,378

{txt}{col 1}Number of strata{col 19}= {res}        1{txt}{col 47}Population size{col 65}={res}  418,786,443
{txt}{col 1}Number of PSUs{col 19}= {res}      918{txt}{col 47}Subpop. no. obs{col 65}={res}   11,079,265
{txt}{col 47}Subpop. size{col 65}={res}            .
{txt}{col 47}Average RVI{col 65}= {res}      0.0001
{txt}{col 47}Largest FMI{col 65}= {res}      0.0002
{txt}{col 47}Complete DF{col 65}= {res}         917
{txt}DF adjustment:{ralign 15: {res:Small sample}}{col 47}DF:     min{col 65}= {res}      914.82
{txt}{col 47}        avg{col 65}= {res}      914.90
{txt}Within VCE type: {ralign 12:{res:Delta-method}}{col 47}        max{col 65}= {res}      914.98

{txt}Expression{col 14}: {res}Pr(Suic), predict(pr)

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:BornUSA}{space 9}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:BornUSA}{space 9}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0000983{col 26}{space 2} 3.08e-06{col 37}{space 1}   31.94{col 46}{space 3}0.000{col 54}{space 4} .0000922{col 67}{space 3} .0001043
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .000153{col 26}{space 2} 1.19e-06{col 37}{space 1}  128.60{col 46}{space 3}0.000{col 54}{space 4} .0001507{col 67}{space 3} .0001554
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 8:lincom}{space 1}{space 1}{ralign 8:se}{space 1}{space 1}{ralign 8:zvalue}{space 1}{space 1}{ralign 8:pvalue}{space 1}{space 1}{ralign 8:ll}{space 1}{space 1}{ralign 8:ul}{space 1}
{space 0}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}
{space 0}{space 0}{ralign 12:1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:0.0000548}}}{space 1}{space 1}{ralign 8:{res:{sf:0.0000036}}}{space 1}{space 1}{ralign 8:{res:{sf: 1.5e+01}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.0e+00}}}{space 1}{space 1}{ralign 8:{res:{sf:0.0000477}}}{space 1}{space 1}{ralign 8:{res:{sf:0.0000618}}}{space 1}
{com}.                         estimates restore m1
{txt}(results {stata estimates replay m1:m1} are active now)
{com}.                         mchange_mi MarStat5 "categorical" "mi"
{res}
{txt}Multiple-imputation estimates{col 47}Imputations{col 65}= {res}          10
{txt}Pairwise comparisons of predictive margins{col 47}Number of obs{col 65}= {res}  11,079,265

{txt}{col 1}Number of strata{col 19}= {res}        1{txt}{col 47}Population size{col 65}={res}  418,786,443
{txt}{col 1}Number of PSUs{col 19}= {res}      918{txt}{col 47}Subpop. no. obs{col 65}={res}   11,079,265
{txt}{col 47}Subpop. size{col 65}={res}            .
{txt}{col 47}Average RVI{col 65}= {res}      0.0163
{txt}{col 47}Largest FMI{col 65}= {res}      0.0269
{txt}{col 47}Complete DF{col 65}= {res}         917
{txt}DF adjustment:{ralign 15: {res:Small sample}}{col 47}DF:     min{col 65}= {res}      831.89
{txt}{col 47}        avg{col 65}= {res}      876.93
{txt}Within VCE type: {ralign 12:{res:Delta-method}}{col 47}        max{col 65}= {res}      910.60

{txt}Expression{col 14}: {res}Pr(Suic), predict(pr)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}MarStat5 {c |}
{space 5}2 vs 1  {c |}{col 14}{res}{space 2} .0001016{col 26}{space 2} 4.21e-06{col 37}{space 5} .0000934{col 51}{space 3} .0001099
{txt}{space 5}3 vs 1  {c |}{col 14}{res}{space 2} .0001638{col 26}{space 2} 3.03e-06{col 37}{space 5} .0001579{col 51}{space 3} .0001698
{txt}{space 5}4 vs 1  {c |}{col 14}{res}{space 2}-.0000273{col 26}{space 2} 6.91e-06{col 37}{space 5}-.0000409{col 51}{space 3}-.0000137
{txt}{space 5}5 vs 1  {c |}{col 14}{res}{space 2} .0000871{col 26}{space 2} 2.78e-06{col 37}{space 5} .0000817{col 51}{space 3} .0000926
{txt}{space 5}3 vs 2  {c |}{col 14}{res}{space 2} .0000622{col 26}{space 2} 4.87e-06{col 37}{space 5} .0000526{col 51}{space 3} .0000717
{txt}{space 5}4 vs 2  {c |}{col 14}{res}{space 2} -.000129{col 26}{space 2} 7.91e-06{col 37}{space 5}-.0001445{col 51}{space 3}-.0001134
{txt}{space 5}5 vs 2  {c |}{col 14}{res}{space 2}-.0000145{col 26}{space 2} 5.11e-06{col 37}{space 5}-.0000246{col 51}{space 3}-4.49e-06
{txt}{space 5}4 vs 3  {c |}{col 14}{res}{space 2}-.0001911{col 26}{space 2} 6.94e-06{col 37}{space 5}-.0002047{col 51}{space 3}-.0001775
{txt}{space 5}5 vs 3  {c |}{col 14}{res}{space 2}-.0000767{col 26}{space 2} 3.73e-06{col 37}{space 5} -.000084{col 51}{space 3}-.0000694
{txt}{space 5}5 vs 4  {c |}{col 14}{res}{space 2} .0001144{col 26}{space 2} 6.66e-06{col 37}{space 5} .0001014{col 51}{space 3} .0001275
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{com}.                 {c )-}
.                 
.                 if (`model_version' == 1 | `model_version' == 2 | `model_version' == 10) {c -(}
.                         estimates restore m1
.                         mchange_mi RAT_Female "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_5 "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 3 | `model_version' == 4) {c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_Sex "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_AgeGrp4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_Race5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_MarStat5 "continuous" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2 | `model_version' == 4 | `model_version' == 9 ){c -(}
.                         estimates restore m1
{txt}(results {stata estimates replay m1:m1} are active now)
{com}.                         mchange_mi UnEmpl "categorical" "mi"
{res}
{txt}Multiple-imputation estimates{col 47}Imputations{col 65}= {res}          10
{txt}Pairwise comparisons of predictive margins{col 47}Number of obs{col 65}= {res}  11,079,265

{txt}{col 1}Number of strata{col 19}= {res}        1{txt}{col 47}Population size{col 65}={res}  418,786,443
{txt}{col 1}Number of PSUs{col 19}= {res}      918{txt}{col 47}Subpop. no. obs{col 65}={res}   11,079,265
{txt}{col 47}Subpop. size{col 65}={res}            .
{txt}{col 47}Average RVI{col 65}= {res}      0.2390
{txt}{col 47}Largest FMI{col 65}= {res}      0.3377
{txt}{col 47}Complete DF{col 65}= {res}         917
{txt}DF adjustment:{ralign 15: {res:Small sample}}{col 47}DF:     min{col 65}= {res}       76.13
{txt}{col 47}        avg{col 65}= {res}      128.97
{txt}Within VCE type: {ralign 12:{res:Delta-method}}{col 47}        max{col 65}= {res}      181.81

{txt}Expression{col 14}: {res}Pr(Suic), predict(pr)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 6}UnEmpl {c |}
{space 5}1 vs 0  {c |}{col 14}{res}{space 2} .0006816{col 26}{space 2} .0000273{col 37}{space 5} .0006273{col 51}{space 3}  .000736
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{com}.                         estimates restore m1
{txt}(results {stata estimates replay m1:m1} are active now)
{com}.                         mchange_mi PhysProb "categorical" "mi"
{res}
{txt}Multiple-imputation estimates{col 47}Imputations{col 65}= {res}          10
{txt}Pairwise comparisons of predictive margins{col 47}Number of obs{col 65}= {res}  11,079,265

{txt}{col 1}Number of strata{col 19}= {res}        1{txt}{col 47}Population size{col 65}={res}  418,786,443
{txt}{col 1}Number of PSUs{col 19}= {res}      918{txt}{col 47}Subpop. no. obs{col 65}={res}   11,079,265
{txt}{col 47}Subpop. size{col 65}={res}            .
{txt}{col 47}Average RVI{col 65}= {res}      0.0104
{txt}{col 47}Largest FMI{col 65}= {res}      0.0203
{txt}{col 47}Complete DF{col 65}= {res}         917
{txt}DF adjustment:{ralign 15: {res:Small sample}}{col 47}DF:     min{col 65}= {res}      861.79
{txt}{col 47}        avg{col 65}= {res}      881.23
{txt}Within VCE type: {ralign 12:{res:Delta-method}}{col 47}        max{col 65}= {res}      900.67

{txt}Expression{col 14}: {res}Pr(Suic), predict(pr)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 4}PhysProb {c |}
{space 5}1 vs 0  {c |}{col 14}{res}{space 2} .0000812{col 26}{space 2} 6.70e-06{col 37}{space 5}  .000068{col 51}{space 3} .0000943
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{com}.                 {c )-}
.         
.                 if (`model_version' == 2 | `model_version' == 10){c -(}
.                         estimates restore m1
.                         mchange_mi RAT_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_PhysProb "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 4){c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_PhysProb "continuous" "mi"
.                 {c )-}               
.         {c )-}
. {c )-}
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         svy: logit Suic i.St `model_eq', or 
.         estimates store m1 
. 
.         if (`model_version' <= 4 | `model_version' == 7 | `model_version' == 8){c -(}
.                 mchange `margin_demographics', amount(all) delta(100) statistics(all) decimals(7)
.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. if (`model_version' == 5 | `model_version' == 6) {c -(}
.         estimates restore m1
.         margin_interact std_same_prop_Sex Female 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_AgeGrp4 AgeGrp4 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_Race5 Race5 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_BornUSA BornUSA 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_MarStat5 MarStat5 5 `model'
. 
.         if (`model_version' == 6) {c -(}
.                 estimates restore m1
.                 margin_interact std_same_prop_UnEmpl UnEmpl 5 `model'
.                 estimates restore m1
.                 margin_interact std_same_prop_PhysProb PhysProb 5 `model'
.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. 
. log close 
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/mi_9.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}24 Aug 2020, 18:22:38
{txt}{.-}
{smcl}
{txt}{sf}{ul off}